Package: GPvecchia
Type: Package
Title: Scalable Gaussian-Process Approximations
Version: 0.1.8
Date: 2026-01-28
Authors@R: c(
	   person("Matthias", "Katzfuss", role=c("aut"), email="katzfuss@gmail.com"),
	   person("Marcin", "Jurek", role=c("aut", "cre"), email="marcinjurek1988@gmail.com"),
	   person("Daniel", "Zilber", role="aut"),
	   person("Wenlong", "Gong", role="aut"),
	   person("Joe", "Guinness", role="ctb"),
	   person("Jingjie", "Zhang", role="ctb"),
	   person("Florian", "Schaefer", role="ctb"))
Maintainer: Marcin Jurek <marcinjurek1988@gmail.com>
Author: Matthias Katzfuss [aut],
  Marcin Jurek [aut, cre],
  Daniel Zilber [aut],
  Wenlong Gong [aut],
  Joe Guinness [ctb],
  Jingjie Zhang [ctb],
  Florian Schaefer [ctb]
Description: Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.
Encoding: UTF-8
License: GPL (>= 2)
Imports: Rcpp (>= 1.1.1), methods, stats, sparseinv, fields, Matrix(>=
        1.5.1), parallel, GpGp, FNN
LinkingTo: Rcpp, RcppArmadillo, BH
RoxygenNote: 7.2.3
Suggests: mvtnorm, knitr, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-01-30 19:25:06 UTC; jurek
Repository: CRAN
Date/Publication: 2026-01-31 07:40:56 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-27 15:00:50 UTC; unix
Archs: GPvecchia.so.dSYM
